{r setup, include=FALSE} knitr::opts_chunk$set(eval = FALSE)
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本文于r format(Sys.Date(), "%Y-%m-%d")更新。 如发现问题或者有建议，欢迎提交 Issue
gcForest包是使用调用python的gcForest的资源。
这是一个新包
{r eval=F,message=FALSE, warning=FALSE} library(tidyverse) library(packagefinder) library(dlstats) library(cranly) sem_pkg <- 'Deep Forest' %>% findPackage() %>% as_tibble() sem_pkg_download <- sem_pkg %>% rename_all(tolower) %>% arrange(desc(score)) %>% distinct(name) %>% # head(100) %>% .$name %>% # 可以插入 vector，所以不需要map cran_stats() sem_pkg_download
数据预处理
{r} sk <- reticulate::import('sklearn')
{r} train_test_split <- sk$model_selection$train_test_split
{r} data <- sk$datasets$load_iris iris <- data() x <- iris$data # matrix y <- iris$target data_split <- train_test_split(x, y, test_size=0.33) x_tr <- data_split[[1]] x_te <- data_split[[2]] y_tr <- data_split[[3]] y_te <- data_split[[4]]
训练模型
```{r} library(gcForest) library(tidyverse) library(lubridate) gcforest_m <- gcforest(shape_1X=4L, window=2L, tolerance=0.0) gcforest_m$fit(x_tr,y_tr) gcf_model <- model_save( gcforest_m ,file.path( ‘files’ ,paste(today() %>% str_remove_all(’-’) %>% str_sub(3,-1),‘gcforest_model.model’,sep=’_’) ) )
gcf <- model_load( file.path( ‘files’ ,list.files(‘files’) %>% str_subset(‘gcforest_model.model’) %>% max ) ) gcf$fit(x_tr, y_tr)
```
gcf$fit(x_tr, y_tr)
预测结果
{r} gcforest_m$predict(x_te) y_te
{r} gcforest_m$predict_proba(x_te)
- 可以看概率